Original Contribution: Stacked generalization
Neural Networks
Case-based reasoning
Data mining and knowledge discovery in databases
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Case-Based Reasoning in the Care of Alzheimer's Disease Patients
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Mediface: Anticipative Data Entry Interface for General Practitioners
OZCHI '98 Proceedings of the Australasian Conference on Computer Human Interaction
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis
Expert Systems with Applications: An International Journal
Executing medical guidelines on the web: Towards next generation healthcare
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
A case-based classifier for hypertension detection
Knowledge-Based Systems
How to combine CBR and RBR for diagnosing multiple medical disorder cases
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Building a case-based diet recommendation system without a knowledge engineer
Artificial Intelligence in Medicine
Computer decision support systems in general practice
International Journal of Information Management: The Journal for Information Professionals
A search problem in complex diagnostic Bayesian networks
Knowledge-Based Systems
Extensible Prototyping for pragmatic engineering of knowledge-based systems
Expert Systems with Applications: An International Journal
Design of an assistive anaesthesia drug delivery control using knowledge based systems
Knowledge-Based Systems
A pattern-based knowledge editing system for building clinical Decision Support Systems
Knowledge-Based Systems
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Objective: With the increased complexity and uncertainty in drug information, issuing medical prescriptions has become a vexing issue. As many as 240,000 medicines are available on the market, so this paper proposes a novel approach to the issuing of medical prescriptions. The proposed process will provide general practitioners (GPs) with medication advice and suggest a range of medicines for specific medical conditions by taking into consideration the collective pattern as well as the individual preferences of physicians' prescription decisions. Methods and material: A hybrid approach is described that uses a combination of case-based reasoning (CBR) and Bayesian reasoning. In the CBR process, all the previous knowledge retrieved via similarity measures is made available for the reference of physicians as to what medicines have been prescribed (to a particular patient) in the past. After obtaining the results from CBR, Bayesian reasoning is then applied to model the prescription experience of all physicians within the organization. By comparing the two sets of results, more refined recommendations on a range of medicines are suggested along with the ranking for each recommendation. Results: To validate the proposed approach, a Hong Kong medical center was selected as a testing site. Through application of the hybrid approach in the medical center for a period of one month, the results demonstrated that the approach produced satisfactory performance in terms of user satisfaction, ease of use, flexibility and effectiveness. In addition, the proposed approach yields better results and a faster learning rate than when either CBR or Bayesian reasoning are applied alone. Conclusion: Even with the help of a decision support system, the current approach to anticipating what drugs are to be prescribed is not flexible enough to cater for individual preferences of GPs, and provides little support for managing complex and dynamic changes in drug information. Therefore, with the increase in the amount of information about drugs, it is extremely difficult for physicians to write a good prescription. By integrating CBR and Bayesian reasoning, the general practitioners' prescription practices can be retrieved and compared with the collective prescription experience as modeled by probabilistic reasoning. As a result, physicians can select the drugs which are supported by informed evidential decisions. That is, they can take into consideration the pattern of decisions made by other physicians in similar cases.